Green energy management in DC microgrids enhanced with Robust Model Predictive Control and Muddled Tuna Swarm MPPT

Abstract

Abstract In recent years, extreme focus on renewable energy has intensified due to environmental concerns and the depletion of fossil fuel supplies. In a DC micro grid that includes photovoltaic (PV), wind, and battery storage systems, this research proposes an integrated strategy for energy management and battery management. The Robust Model Predictive Control (RMPC) method is proposed to deal with uncertainties and disturbances while offering the best possible control options. A comparison of the two algorithms reveals that the RMPC performs better than the conventional MPC method. To harvest the most solar electricity from the PV system, a sophisticated MPPT optimisation technique called Muddled Tuna Swarm Optimisation (MTSO) is applied. Drone Squadron Optimisation (DSO) and Slime Mould Optimisation (SMO) are outperformed by MTSO in terms of dynamic performance, effectively monitoring the maximum power point (MPP) of the PV system, and increasing overall energy output. The suggested RMPC approach and MTSO technique are effective in achieving optimal energy and battery management as well as maximum solar power extraction, according to the simulation findings. On the OPAL-RT platform, real-time simulation is used to test the control strategy.